Blog Post:When learning to skydive, there is a certain amount of training that has to be done on the ground. Anyone can jump in tandem (attached to someone who has done all the training), but becoming a licensed skydiver means gaining some experience. That experience is partially book work and a lot of practice. Being an effective analyst requires a lot of the same tenacity and hard work, which is good because I wasn’t quite ready to abandon this analogy just yet.
Familiarity
When beginning any sort of process, be it skydiving, predictive forecasting, or even lion taming, it is imperative to understand what you are getting into. Familiarizing yourself with the predicative analytics maturity model in part is not enough. In some ways, learning just a portion of the model or only implementing it partially, is a lot like skydiving with only some of the harness being secured. Who does that? When the chute opens (or you gain some executive traction), you’re most likely going to fall to your death or, even worse, not have answers for your boss.
In a skydiving ground school situation, you could be learning how to strap on your harness, how to pack your chute, and perhaps even the limits of the human body. With analytics ground school you are learning the first steps in applying the tools of the trade. Becoming familiar with your tools and knowing the basics are the keys to continued success in analytics.
I think back to Tim Tebow (I can use more than one analogy, it’s my blog) coming into the NFL. Tim’s passer rating (his rating based on accuracy and production as a passer—hard numbers, mind you) surpassed Peyton Manning, Eli Manning, and Tim Couch, all three of which are premier quarterbacks in the NFL. So if Tim Tebow had great success at Florida, why was he largely ineffective once he entered into the professional league?
As it turns out, throwing the football side arm versus throwing over the shoulder takes something like 0.6 seconds more. That six-tenths of a second was enough to limit his effectiveness in the NFL. While he could get away with having a slower delivery in college, the level of talent in the NFL is such that a quarterback (or any position) must be at the height of efficiency. For him to be more efficient, Tebow had to revisit the basics and relearn some fundamentals in order to compete at an elite level. It’s not just Mr. Tebow either; all athletes practice the basics over and over again. Why? Because they use them during every game. The same holds true for analysts as well; knowing the basics and practicing them over and over again is the key to continued success. The next question on your mind might be, “Where do I practice?”
Identify
It is difficult to gain any sort of executive buy-in by trying to affect your entire business all at once. The key really comes in with identifying smaller, more self-contained parts of your business and performing the basics in a practice mode. How does this help you align your organization? Furthermore, if you cannot communicate the output to the executives in your company, how can you get them to understand the value of an analytics program? The answer is, you can’t. The solution to overcoming the initial hurdles is fairly simple.

Identify your spheres of influence. What processes can you affect without those processes impacting anything else? Typically, there are small micro-chasms within any organization that can be tweaked a little here and there. Once you have identified these processes within your organization, move on to the next step.

Take an aggregate look at the list of likely candidates. Which processes are you most likely going to see an output from, and which are going to be harder to show forward progress and productivity? You need to identify the processes that will net measurable value. It’s not enough to streamline something and then set back and let your new process go to work. The end result of not having a measurable outcome is looking like you haven’t done anything at all. Having measurable metrics (before and after your adjustments) means showing value.

Have an execution plan in place. A slide presentation might look good in the board room, but unless you can execute a plan to solve a problem or improve the process, all you have really done is cause yourself grief.

Execute, execute, execute.

Gather Your Tools
Once you’ve identified the process you will be working with, it’s a good idea to gather and become comfortable with the tools you will be using. Adobe Analytics Standard, for example, has anomaly detection built right into the software. This is a great tool to use in the beginning.
Here’s an example of where to start. I might see that visits to lead form page drop off. Let’s say there used to be 1,000 leads a day, but the leads have dropped to nearly zero overnight. While this would normally be something that would crop up on weekly analytics report or dashboard, solving the problem quickly means not losing traction. So if the analyst detects the anomaly more quickly and identifies the cause, the company wins. Obviously, you can show what would have happened had you not interceded and fixed a broken or not well-executed process.
Think about where you can affect small and meaningful change within your organization, and begin using the tools and processes that will help you succeed. Practice your fundamentals and never take them for granted. In my next post ,we will outline some of the ways you can add to what you’re already doing and begin to add the layers that will create a formidable and profitable predictive analytics plan.
Author:John Bates
Date Created:January 22, 2014
Headline:Analytic Solutions: Ground School
Social Counts:
Keywords: #analytic solution #analytics program #predicative analytics #predictive forecasting
Publisher:Adobe

Analytic Solutions: Ground School

When learning to skydive, there is a certain amount of training that has to be done on the ground. Anyone can jump in tandem (attached to someone who has done all the training), but becoming a licensed skydiver means gaining some experience. That experience is partially book work and a lot of practice. Being an effective analyst requires a lot of the same tenacity and hard work, which is good because I wasn’t quite ready to abandon this analogy just yet.

Familiarity

When beginning any sort of process, be it skydiving, predictive forecasting, or even lion taming, it is imperative to understand what you are getting into. Familiarizing yourself with the predicative analytics maturity model in part is not enough. In some ways, learning just a portion of the model or only implementing it partially, is a lot like skydiving with only some of the harness being secured. Who does that? When the chute opens (or you gain some executive traction), you’re most likely going to fall to your death or, even worse, not have answers for your boss.

In a skydiving ground school situation, you could be learning how to strap on your harness, how to pack your chute, and perhaps even the limits of the human body. With analytics ground school you are learning the first steps in applying the tools of the trade. Becoming familiar with your tools and knowing the basics are the keys to continued success in analytics.

I think back to Tim Tebow (I can use more than one analogy, it’s my blog) coming into the NFL. Tim’s passer rating (his rating based on accuracy and production as a passer—hard numbers, mind you) surpassed Peyton Manning, Eli Manning, and Tim Couch, all three of which are premier quarterbacks in the NFL. So if Tim Tebow had great success at Florida, why was he largely ineffective once he entered into the professional league?

As it turns out, throwing the football side arm versus throwing over the shoulder takes something like 0.6 seconds more. That six-tenths of a second was enough to limit his effectiveness in the NFL. While he could get away with having a slower delivery in college, the level of talent in the NFL is such that a quarterback (or any position) must be at the height of efficiency. For him to be more efficient, Tebow had to revisit the basics and relearn some fundamentals in order to compete at an elite level. It’s not just Mr. Tebow either; all athletes practice the basics over and over again. Why? Because they use them during every game. The same holds true for analysts as well; knowing the basics and practicing them over and over again is the key to continued success. The next question on your mind might be, “Where do I practice?”

Identify

It is difficult to gain any sort of executive buy-in by trying to affect your entire business all at once. The key really comes in with identifying smaller, more self-contained parts of your business and performing the basics in a practice mode. How does this help you align your organization? Furthermore, if you cannot communicate the output to the executives in your company, how can you get them to understand the value of an analytics program? The answer is, you can’t. The solution to overcoming the initial hurdles is fairly simple.

Identify your spheres of influence. What processes can you affect without those processes impacting anything else? Typically, there are small micro-chasms within any organization that can be tweaked a little here and there. Once you have identified these processes within your organization, move on to the next step.

Take an aggregate look at the list of likely candidates. Which processes are you most likely going to see an output from, and which are going to be harder to show forward progress and productivity? You need to identify the processes that will net measurable value. It’s not enough to streamline something and then set back and let your new process go to work. The end result of not having a measurable outcome is looking like you haven’t done anything at all. Having measurable metrics (before and after your adjustments) means showing value.

Have an execution plan in place. A slide presentation might look good in the board room, but unless you can execute a plan to solve a problem or improve the process, all you have really done is cause yourself grief.

Execute, execute, execute.

Gather Your Tools

Once you’ve identified the process you will be working with, it’s a good idea to gather and become comfortable with the tools you will be using. Adobe Analytics Standard, for example, has anomaly detection built right into the software. This is a great tool to use in the beginning.

Here’s an example of where to start. I might see that visits to lead form page drop off. Let’s say there used to be 1,000 leads a day, but the leads have dropped to nearly zero overnight. While this would normally be something that would crop up on weekly analytics report or dashboard, solving the problem quickly means not losing traction. So if the analyst detects the anomaly more quickly and identifies the cause, the company wins. Obviously, you can show what would have happened had you not interceded and fixed a broken or not well-executed process.

Think about where you can affect small and meaningful change within your organization, and begin using the tools and processes that will help you succeed. Practice your fundamentals and never take them for granted. In my next post ,we will outline some of the ways you can add to what you’re already doing and begin to add the layers that will create a formidable and profitable predictive analytics plan.

John Bates

John Bates is the Senior Product Manager for Data Science & Predictive Marketing Solutions for the Adobe® Marketing Cloud. His core responsibility is to develop the software product roadmap for all advanced analytics and data science capabilities (i.e., including advanced statistics, data mining, predictive modeling, machine learning and text mining/natural language processing solutions) found within the products of Adobe's Marketing Cloud. Before joining Adobe Product Management, Bates founded and managed the Predictive Analytics Consulting practice for Adobe Consulting - consulting with some of the world’s largest companies and brands using data mining and predictive modeling techniques in order to drive greater digital marketing success. Bates has a B.A. in Economics from Brigham Young University and a M.S. in Predictive Analytics from Northwestern University.